writer.add_scalar('test_loss', test_loss, epoch)
writer.add_scalar('test_acc', test_acc, epoch)
# if epoch >= 5:
# scheduler.step(test_loss)
print(optimizer)
print(log_dir)
for idx, q_type in idx_to_question_type.items():
writer.add_scalar('train_acc_sub_{}'.format(q_type),
train_acc_list[idx], epoch)
writer.add_scalar('train_loss', train_loss, epoch)
writer.add_scalar('train_acc', train_acc, epoch)
saved_model_list = [x for x in os.listdir(model_dir) if x.endswith('.pt')]
saved_model_list = sorted(
saved_model_list, key=lambda x: int(x.split('_')[-1].split('.pt')[0]))
if len(saved_model_list) >= 5:
os.remove(os.path.join(model_dir, saved_model_list[0]))
if epoch % 5 == 0:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict()
}, os.path.join(model_dir, 'model_{}.pt'.format(epoch)))
